Fuzzy clustering ensemble based on mutual information

Abstract

-Clustering ensemble is a new topic in machine learning. It can find a combined clustering with better quality from multiple partitions. But how to find the combined clustering is a difficult problem. In this paper, we extend the object function proposed by Strehl & Ghosh which is based on mutual information and we present a new algorithm similar to information bottleneck to solve the object function. This algorithm can combine “soft” partitions and need not establish label correspondence between different partitions. We conducted experiments on four real-world data sets to compare our algorithm with other five ensemble algorithms, including CSPA, HGPA, MCLA, QMI. The results indicate that our algorithm provides solutions of improved quality. Key-Words: Clustering Ensemble, Mutual Information, soft partitions

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Cite this paper

@inproceedings{Gao2006FuzzyCE, title={Fuzzy clustering ensemble based on mutual information}, author={Yan Gao and Shiwen Gu and Liming Xia and ZHINING NIAO}, year={2006} }